Master'sMotorcycles & 2-Wheelers Applications open

Master’s in Sensor Calibration and Self-Calibration

Online master's programme of 1,900 hours in sensor calibration and self-calibration. 12 modules covering metrology, measurement uncertainty, laboratory calibration, inertial and perception sensors, online self-calibration, multi-sensor calibration, industrial sensors, machine learning, quality systems and calibration management, applying JCGM 100:2008 (GUM), ISO/IEC 17025 and MATLAB, with an assigned technical tutor.

Duration12–14 months

ModeOnline

LanguageEnglish

Start datesSet by the faculty

AwardSEIUM Institute diploma

Enrol now View syllabus Ask an advisor

Master’s in Sensor Calibration and Self-Calibration — studying from Australia

Motorcycles and two-wheeled vehicles are a significant part of Australian transport and recreation, with strong demand for motorcycles, scooters and e-bikes in urban and regional areas. Engineers working in this field contribute to chassis and powertrain design, rider safety systems and emissions compliance. National vehicle standards and rider safety initiatives shape product development and testing. Employers include motorcycle importers and distributors, component suppliers, accessory makers and e-mobility start-ups.

Fees for Australia: A$9,410 (AUD), with instalments and employer sponsorship available.

Relevant bodies in Australia

Federal Chamber of Automotive Industries (FCAI)
Australian Motorcycle Industry Association
Standards Australia
Department of Infrastructure, Transport, Regional Development, Communications and the Arts

Typical roles

Motorcycle Design Engineer
Two-Wheeler Test Engineer
Rider Safety Engineer
E-Mobility Product Engineer

Questions from Australia

Will a SEIUM qualification be recognised for motorcycle engineering roles in Australia?

SEIUM qualifications are private and not accredited by any Australian national body. Recognition for engineering or vehicle compliance roles is decided by individual employers and relevant bodies. Vehicle safety and compliance in Australia are governed by national design rules, which apply regardless of your qualification, so check requirements with the employer.

Which Australian employers work in motorcycle and two-wheeler engineering?

Motorcycle and scooter importers, distributors, component suppliers, dealer service networks and performance workshops are examples of Australian employers in this field. Whether they value a private qualification depends on their policies. Practical experience and a strong final project portfolio are typically persuasive in this industry.

Is the course suitable for someone working full time in Australia?

The programmes are delivered 100% online in English with an assigned technical tutor, so you can study from home at times that suit you. Fees are payable in instalments, and employers may sponsor you. Check the hours required for your chosen programme and make sure your schedule allows steady progress.

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Learning outcomes

What you will be able to do

Apply the concepts of metrology and traceability.

Calculate measurement uncertainty according to the GUM.

Calibrate sensors in the laboratory.

Calibrate inertial and perception sensors.

Design self-calibration algorithms.

Calibrate multi-sensor systems.

Manage calibration programmes within a quality system.

Profile

Who it's for

Calibration engineers
Metrology technicians
Sensor fusion engineers
Perception engineers
Calibration laboratory managers
Instrumentation engineers
Engineering or science graduates looking to move into the automotive and ADAS sector

Standards & tools

What you'll work with

JCGM 100:2008 (GUM) (guide to the expression of uncertainty in measurement)
ISO/IEC 17025 (competence of testing and calibration laboratories)
ISO 10012 (measurement management systems)
JCGM 200:2012 (VIM) (international vocabulary of metrology)
ISO 16063-21 (vibration calibration by comparison to a reference transducer)
IEEE 952 (specification and test procedure for fibre-optic gyros)
ISO 26262 (road vehicle functional safety)
AIAG MSA (measurement systems analysis)
MATLAB
OpenCV
Kalibr
ROS 2
Python

Syllabus

12 modules

1.1International vocabulary of metrology
1.2Metrological traceability to standards
1.3Accuracy, precision and resolution
1.4Systematic and random errors
1.5Classification of sensors
1.6Transfer functions
1.7Non-linearity and hysteresis
1.8Time and thermal drift
1.9Quality systems in metrology
1.10Case study: datasheet of a sensor

2.1The GUM guide to the expression of uncertainty
2.2Type A and Type B evaluation
2.3Propagation of uncertainties
2.4Sensitivity coefficients
2.5Expanded uncertainty and coverage
2.6Monte Carlo method
2.7Uncertainty budgets
2.8Conformity with tolerances
2.9Decision rules
2.10Practical exercise: uncertainty budget for a transducer

3.1Standards and calibration hierarchy
3.2Temperature calibration
3.3Pressure calibration
3.4Force and torque calibration
3.5Acceleration calibration
3.6Calibration of electrical quantities
3.7Calibration intervals
3.8Calibration certificates
3.9ISO/IEC 17025 accreditation
3.10Lab: calibration of a thermocouple

4.1MEMS accelerometers and their errors
4.2MEMS and fibre-optic gyroscopes
4.3IMU error models
4.4Multi-position IMU calibration
4.5Rate table calibration
4.6Allan variance
4.7Magnetometers and hard-iron and soft-iron calibration
4.8GNSS sensors and biases
4.9Inertial fusion with other sensors
4.10Practical exercise: Allan variance of an IMU

5.1Cameras and the pinhole model
5.2Intrinsic camera calibration
5.3Lens distortion
5.4Radar and its calibration
5.5LiDAR and error models
5.6Ultrasonic proximity sensors
5.7Extrinsic calibration between sensors
5.8Calibration targets and patterns
5.9End-of-line calibration
5.10Lab: camera calibration with OpenCV

6.1The self-calibration concept
6.2Real-time bias estimation
6.3Extended Kalman filters for calibration
6.4Calibration using natural motion
6.5Targetless calibration
6.6Detection of miscalibration
6.7Recalibration after maintenance
6.8Robustness and observability
6.9Limitations of self-calibration
6.10Case study: online estimation of IMU biases

7.1Time synchronisation of sensors
7.2Camera–LiDAR calibration
7.3Radar–camera calibration
7.4IMU–camera calibration
7.5Least-squares optimisation
7.6Kalibr and similar tools
7.7Calibration validation
7.8Quality metrics
7.9Calibration of vehicle fleets
7.10Practical exercise: camera–LiDAR calibration

8.1Flow and level sensors
8.2Gas analysers
8.3Smart sensors and HART
8.4On-site calibration of instruments
8.5Verification without removal
8.6Condition-based maintenance
8.7Instrument management in the plant
8.8Functional safety of sensors
8.9Cybersecurity of instruments
8.10Case study: calibration plan for a plant

9.1Data-driven calibration
9.2Thermal compensation with neural networks
9.3Anomaly detection in sensors
9.4Virtual sensor calibration
9.5Soft or virtual sensors
9.6Transfer between sensors
9.7Model explainability
9.8Validation of calibration models
9.9Integration into embedded systems
9.10Lab: thermal compensation with regression

10.1ISO/IEC 17025 in laboratories
10.2ISO 10012 for measurement management systems
10.3Legal metrology requirements
10.4IATF 16949 automotive requirements
10.5Measurement system analysis (MSA)
10.6Sensor requirements in ADAS
10.7Aerospace calibration standards
10.8Calibration documentation
10.9Metrology audits
10.10Workshop: calibration procedure suitable for accreditation

11.1Calibration programmes
11.2Equipment management software
11.3Calibration costs
11.4Outsourcing of calibrations
11.5Laboratory indicators
11.6Metrological risk management
11.7Technician training
11.8Trends in sensors
11.9Digitalisation of certificates
11.10Case study: calibration management for a fleet

12.1Selection of the sensor system
12.2Accuracy requirements
12.3Error model
12.4Calibration procedure
12.5Self-calibration algorithm
12.6Experimental validation of the system
12.7Uncertainty budget
12.8Technical conclusions and recommendations
12.9Master's Thesis report
12.10Master's Thesis: defence before a panel

Methodology, software & labs

How you'll learn

100% online training on the SEIUM campus, with downloadable material, recorded classes and an assigned technical tutor. No fixed timetables: you progress at your own pace within each intake, so it can be combined with work.

Each module combines theory, guided reading of the original standard and applied exercises. The working software is MATLAB, OpenCV, Kalibr, ROS 2 and Python, in free, academic or evaluation versions. Testing is studied through real procedures: laboratory sensor calibration, camera–LiDAR multi-sensor calibration and online self-calibration algorithms. Assessment is continuous by module plus the Master's Thesis.

Standards covered: JCGM 100:2008 (GUM), ISO/IEC 17025, ISO 10012, JCGM 200:2012 (VIM), ISO 16063-21, IEEE 952, ISO 26262, AIAG MSA.

Assigned technical tutor:every module ends with a deliverable that your tutor marks individually.

Standards-based learning:guided reading of JCGM 100:2008 (GUM), ISO/IEC 17025 and ISO 10012 applied to real cases, not as a theoretical annex.

Industry software:MATLAB, OpenCV, Kalibr, ROS 2 and Python, in free, academic or evaluation versions.

Master's Thesis with defence:Design of a calibration and self-calibration system for a real sensor set, from error model to experimental validation and uncertainty budget, defended before a panel.

Flexible format:100% online, no fixed timetables, compatible with full-time work.

Capstone project

Build it end to end

Your final project follows the real engineering workflow, step by step, reviewed by your tutor.

Requirements and error model

Define the accuracy requirements and error model of the selected sensor system.

Calibration procedure

Develop the calibration procedure for the system.

Self-calibration algorithm

Design the online self-calibration algorithm.

Validation and uncertainty

Validate the system experimentally and prepare the uncertainty budget.

Master's Thesis

Calibration and self-calibration system design, defended before a panel.

Career opportunities

Where this takes you

Calibration engineerMetrology technicianSensor fusion engineerPerception engineerCalibration laboratory managerInstrumentation engineer

Admissions & scholarships

Requirements

A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.
An internet connection and a computer capable of running the programme software.
English sufficient to read technical standards.

Ready to start?

Send your application today. Our admissions team replies within 72 hours with your study plan and funding options.

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FAQ

Frequently asked questions

The programme totals 1,900 hours over 12–14 months. You set your own pace within each intake, and your tutor helps you plan the workload around your job.

You work with MATLAB, OpenCV, Kalibr, ROS 2 and Python, using free, academic or evaluation versions. Your tutor indicates the free alternative wherever one exists.

A SEIUM Institute diploma, awarded on passing the continuous assessment and the Master's Thesis. It is SEIUM's own qualification, not an official state degree; its recognition depends on the programme completed and the regulations of each country.

Yes. Modules passed in a course or postgraduate diploma in the same area are recognised in the higher programme. Ask about the recognition policy when you enrol.

A university degree in engineering, science or architecture, or equivalent demonstrable professional experience.

Start dates are set by SEIUM's faculty for each intake. Our admissions team will confirm the next available date when you apply.

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